PaTeCon

This is the code for our AAAI 2023 paper PaTeCon: A Pattern-Based Temporal Constraint Mining Method for Conflict Detection on Knowledge Graphs.

Because we are still improving the code, the experimental results may be slightly different from the paper.

Installation

To get source code, run:

git clone https://github.com/JianhaoChen-nju/PaTeCon.git

Install requirements

cd PaTeCon
conda create -n PaTeCon python=3.9
conda activate PaTeCon
pip install -r requirements.txt

Dataset

Our datasets WD50K, WD27M and FB37M can be downloaded in url Google Drive URL. You can also find the original WD50k in https://github.com/dwslab/TeCoRe/tree/master/conf/resources/rockit.

Our data is organized as follows:

subject	property	object	start time	end time

If start time or end time value == "null", it means that the time value is unknown or doesn't exist.

In this Google Drive URL, you can also find the type files needed for refinements: wikidata-entity-type-info.tsv and freebase-entity-type-info.tsv.

The type file is organized as follows:

entity	entity type 1	entity type 2	entity type ...

Download all above files to PaTeCon/resource folder.

Running the code

Constraint Mining:

run WD50K :

python Constraint_Mining.py --dataset=resource/WD50K.tsv --knowledgegraph=wikidata --support=10 --candidate_confidence=0.5 --confidence=0.9

run WD27M:

python Constraint_Mining.py --dataset=resource/WD27M.tsv --knowledgegraph=wikidata --refinement=True --typefile=resource/wikidata-entity-type-info.tsv --support=100 --candidate_confidence=0.5 --confidence=0.9

run FB37M:

python Constraint_Mining.py --dataset=resource/FB37M.tsv --knowledgegraph=freebase --refinement=True --typefile=resource/freebase-entity-type-info.tsv --support=100 --candidate_confidence=0.5 --confidence=0.9

Parameters to choose:

dataset, knowledgegraph, refinement, typefile, support, candidate_confidence, confidence

dataset: the dataset you want to mine constraints on.

knowledgegraph: Which type of knowledge graph the dataset is. We set three types: wikidata, freebase and other.

refinement: Whether to mine refined constraints. The default value of refinement is set to False. If you want to mine refined constraints, you should provide entity type files.

typefile: The entity type files needed during refinement stage. If you need to mine refined constraints on your own dataset, you need to provide typefile just like ours.

support: The positive instances of constraints. The default value is 100. If the dataset is small, it is recommended to adjust the support smaller such as 20/50.

candidate_confidence: The candidate confidence of constraints. The default value is 0.5. We don't think it is usually necessary to modify the value of candidate_confidence.

confidence: The final confidence of constraints. Generally speaking, confidence=0.9 can get reliable constraints. Of course, you can also relax appropriately to get more constraints. Or increase confidence to more than 0.95 to get more correct constraints.

Output:

We will output all candidate constraints in file "output/"+dataset+"_rules". Refined rules are in file "output/"+dataset+"_refined_rules".

The final constraints are in file "output/"+dataset+"_all_constraints".

Conflict Detection:

run WD50K :

python Conflict_Detection.py --dataset=resource/WD50K.tsv --knowledgegraph=wikidata --constraint=output/WD50K.all_constraints

run WD27M

python Conflict_Detection.py --dataset=resource/WD27M.tsv --knowledgegraph=wikidata --constraint=output/WD27M.all_constraints --refinement=True --typefile=resource/wikidata-entity-type-info.tsv

run FB37M

python Conflict_Detection.py --dataset=resource/FB37M.tsv --knowledgegraph=freebase --constraint=output/FB37M.all_constraints --refinement=True --typefile=resource/freebase-entity-type-info.tsv

Parameters to choose:

dataset, knowledgegraph, constraint, refinement, typefile

dataset: the dataset you want to detect conflicts on.

knowledgegraph: Which type of knowledge graph the dataset is. We set three types: wikidata, freebase and other.

constraints: The constraint file used to detect conflicts.

refinement: Whether to mine refined constraints.

typefile: The entity type files needed during refinement stage.

Output:

We will output conflicts which are detected by non-refined constraints in file "output/"+dataset+"_conflict". Conflicts detected by refined constraints are in file "output/"+dataset+"_refined_conflict".

All conflicts are in file "output/"+dataset+"_all_conflicts".

Constraint and conflict form

Constraints:

Our constraints are organized in the form of body => head.

For example:

(a,P39,b,t1,t2) & (a,P22,c,t3,t4) & (c,P569,d,t5,t6) => before(t5,t6,t1,t2)|0.9916566033593149								(1)

At output we output a simplified form of the constraint along with its confidence as follows.

a,P22*P569,d,t5,t6 before a,P39,b,t1,t2|0.9916566033593149																	(2)

(2) is the simple form of (1). The two can be transformed into each other. The "|" is followed by the confidence of the constraint "0.9916566033593149".

"*" represents the compounding of relationships. The variable c is hidden after compounding in the simple form.

A more complex example:

(a,P54,b,t1,t2) & (b,class,Q6979593) & (a,P54,c,t3,t4) & (c,class,Q6979593) => disjoint(t1,t2,t3,t4)|0.9577199311039615		(3)

At output we output a simplified form of the constraint along with its confidence as follows.

a,P54,b,t1,t2 b,class,Q6979593 disjoint a,P54,c,t3,t4 c,class,Q6979593|0.9577199311039615									(4)

(4) is the simple form of (3). The two can be transformed into each other. The "|" is followed by the confidence of the constraint "0.9577199311039615".

(b,class,Q6979593) means the variable b's class is Q6979593.

Conflicts:

Our conflicts are represented in the form of "constraint fact1 fact2".

An example is as follows:

a,P569,b,t1,t2 MutualExclusion a,P569,c,t3,t4|0.9934953327578068    Q1611104,P569,1885-05-16 00:00:00Z,18850516,18850516   Q1611104,P569,1895-05-16 00:00:00Z,18950516,18950516

This example explains that Q1611104 has two birthdays, which violates the constraint a,P569,b,t1,t2 MutualExclusion a,P569,c,t3,t4|0.9934953327578068.

How to cite

If you used our work or found it helpful, please use the following citation:

@inproceedings{chen2023patecon,
  title={PaTeCon: a pattern-based temporal constraint mining method for conflict detection on knowledge graphs},
  author={Chen, Jianhao and Ren, Junyang and Ding, Wentao and Qu, Yuzhong},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={37},
  number={4},
  pages={4166--4172},
  year={2023}
}